# Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation

arXiv: https://arxiv.org/abs/2404.17820 · HTML: https://mlsanity.com/paper/2404.17820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 27 Apr 2024.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 14 of 14)

- [Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles](https://mlsanity.com/paper/1910.04586/) (2019)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability](https://mlsanity.com/paper/2209.10788/) (2022)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation](https://mlsanity.com/paper/1710.05627/) (2017)
- [Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving](https://mlsanity.com/paper/2005.11470/) (2020)
- [Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data](https://mlsanity.com/paper/2210.14721/) (2022)
- [OVPC Mesh: 3D Free-space Representation for Local Ground Vehicle Navigation](https://mlsanity.com/paper/1811.10266/) (2018)
- [Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning](https://mlsanity.com/paper/2109.07150/) (2021)
- [Predicting Energy Consumption of Ground Robots On Uneven Terrains](https://mlsanity.com/paper/2212.06393/) (2022)
- [Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression](https://mlsanity.com/paper/1910.03633/) (2019)
- [Fast Local Planning and Mapping in Unknown Off-Road Terrain](https://mlsanity.com/paper/1910.08521/) (2019)
- [Complex Terrain Navigation via Model Error Prediction](https://mlsanity.com/paper/2111.09768/) (2021)
- [NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces](https://mlsanity.com/paper/2207.13647/) (2022)

## Built on by (most cited first, 1 of 1)

- [Autonomous Driving in Unstructured Environments: How Far Have We Come?](https://mlsanity.com/paper/2410.07701/) (2024)

## Abstract (arXiv)

Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multi-layer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a CNN-LSTM network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.